mean Dice: 0.9488mIoU: 0.9065F-measure: 0.9513Precision: 0.9713
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Medical Image Segmentation
Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.
<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>
논문
mean Dice: 0.918mIoU: 0.868
mean Dice: 0.92mIoU: 0.871
mean Dice: 0.9445mIoU: 0.8974
Average MAE: 0.021mean Dice: 0.915S-Measure: 0.923max E-Measure: 0.972
Average MAE: 0.023mean Dice: 0.918S-Measure: 0.929max E-Measure: 0.968
mean Dice: 0.927mIoU: 0.877
Average MAE: 0.023mean Dice: 0.924mIoU: 0.876
mean Dice: 0.928
mean Dice: 0.931mIoU: 0.887
mean Dice: 0.9420mIoU: 0.8973
mean Dice: 0.9385mIoU: 0.8903
mean Dice: 0.947mIoU: 0.899
mean Dice: 0.9274mIoU: 0.8790
mean Dice: 0.9263mIoU: 0.8843
mean Dice: 0.9363mIoU: 0.8894
mean Dice: 0.9409mIoU: 0.8912Precision: 0.8964Recall: 0.9038
mean Dice: 0.9258mIoU: 0.8776
mean Dice: 0.939mIoU: 0.885
mean Dice: 0.9217mIoU: 0.8914
mean Dice: 0.927mIoU: 0.873
mean Dice: 0.932
mean Dice: 0.915mIoU: 0.862
mean Dice: 0.9411mIoU: 0.9002
mean Dice: 0.9357mIoU: 0.8905
mean Dice: 0.928mIoU: 0.881
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection
mean Dice: 0.915mIoU: 0.857
mean Dice: 0.9502mIoU: 0.9051Precision: 0.9628Recall: 0.9379